### MERGE spain DATA
### Edited by FH
rm(list = ls()); source("./R/format.function.R"); library(reshape)

#########################
## READ DATA
####################
### read individuals tree data
#data.spain <- read.table('./data/raw/DataSpain/Tree_data_SFI.txt',header=TRUE,stringsAsFactors=FALSE,sep = "\t")
data.spain <- read.table('./data/raw/DataSpain/Tree_data_SFI_aug13_alldata.txt',header=TRUE,stringsAsFactors=FALSE,sep = "\t")

######################################
## MASSAGE TRAIT DATA
############################
## Compute maximum height per species plus sd from observed height to add variables to the traits data base
## Because we have two heights, then take the max of the two heights and then bootstrap 
res.quant.boot <- t(sapply(levels(factor(data.spain[["SP_code"]])),FUN=f.quantile.boot,R=1000,x=log10(apply(data.spain[,c("ht1","ht2")],1,max,na.rm=T)),fac=factor(data.spain[["SP_code"]])))

## create data base
data.max.height <- data.frame(code=rownames(res.quant.boot),Max.height.mean=res.quant.boot[,1],Max.height.sd=res.quant.boot[,2],Max.height.nobs=res.quant.boot[,3])
rm(res.quant.boot)
write.csv(data.max.height,file="./data/process/data.max.height.spain.csv") # I was planning to save processed data in that folder
# 
# ## merge TRY with max height
# merge.TRY <- merge(merge.TRY,data.max.height,by="code")
# rm(data.max.height)
# ## use mean sd of max tree height over all species
# merge.TRY$Max.height.sd.1 <- rep(mean(merge.TRY[["Max.height.sd"]],na.rm=TRUE),length=nrow(merge.TRY))
# 
# ### keep only variables needed in traits data
# names.traits.data <- c("code","Latin_name","Leaf.N.mean","Seed.mass.mean","SLA.mean","Wood.Density.mean",
#   "Leaf.Lifespan.mean","Max.height.mean","Leaf.N.sd.1","Seed.mass.sd.1","SLA.sd.1", "Wood.Density.sd.1",
#   "Leaf.Lifespan.sd.1","Max.height.sd.1")
# 
# data.traits <- merge.TRY[,names.traits.data]
# names(data.traits) <- c("sp","Latin_name","Leaf.N.mean","Seed.mass.mean","SLA.mean","Wood.Density.mean",
#   "Leaf.Lifespan.mean","Max.height.mean","Leaf.N.sd","Seed.mass.sd","SLA.sd", "Wood.Density.sd",
#   "Leaf.Lifespan.sd","Max.height.sd") ## rename to have standard variables name
# rm(merge.TRY,names.traits.data)

################################################################
## FORMAT INDIVIDUAL TREE DATA
#############

## change unit and names of variables to be the same in all data for the tree 
data.spain$G <- data.spain[["adbh"]] ## diameter growth in mm per year
data.spain$year <- rep(NA,length(data.spain[["adbh"]])) ## number of year between measurement - MISSING
data.spain$D <- data.spain[["dbh1"]]/10 ## diameter in mm convert to cm
data.spain$dead <- as.numeric(data.spain[["Life_status"]] == "dead") ## dummy variable for dead tree 0 alive 1 dead - MIGHT WANT TO CHANGE THIS TO BE BASED ON MORTALITY_CUT
data.spain$sp <- as.character(data.spain[["SP_code"]]) ## species code
data.spain$plot <- (data.spain[["Plot_ID_SFI"]]) ## plot code
data.spain$htot <- data.spain[["ht1"]]## height of tree in m
data.spain$tree.id <- data.spain$Tree_ID_SFI ## tree unique id

#### change coordinates system of x y to be in lat long WGS84/don't know how to do this
library(sp); library(dismo); library(rgdal); 
data.sp <-  data.spain[,c("Tree_ID_SFI","CX","CY")]
coordinates(data.sp) <- c("CX", "CY") # define x y
proj4string(data.sp) <- CRS("+init=epsg:23030")  # define projection system of our data ## EPSG CODE 23030 ED50 / UTM zone 30N
summary(data.sp)

detach(package:rgdal)
data.sp2 <- spTransform(data.sp,CRS("+init=epsg:4326")) ## change projection in WGS84 lat lon
data.spain$Lon <- coordinates(data.sp2)[,"CX"]
data.spain$Lat <- coordinates(data.sp2)[,"CY"]
## ## plot on world map
## library(rworldmap)
## newmap <- getMap(resolution = "coarse")  # different resolutions available
## plot(newmap)
## points(data.sp2,cex=0.2,col="red")
rm(data.sp,data.sp2)

######################
## ECOREGION
###################
## merge greco to have no ecoregion with low number of observation
greco <- read.csv(file = "./data/raw/DataSpain/R_Ecoregion.csv", header = T) 
greco <- greco[,c("Plot_ID_SFI","BIOME","eco_code")]
greco2 <- greco[!duplicated(greco$Plot),]; 
rm(greco)

data.spain <- merge(data.spain, greco2, by = "Plot_ID_SFI")
rm(greco2)

table(data.spain$eco_code)
## There's an eco-region with no code, and one with < 1000 sites
## The former we could drop as they were on the border of Spain

library(RColorBrewer); mycols <- brewer.pal(10,"Set3"); 
ecoreg <- unclass(data.spain$eco_code); 
plot(data.spain[["CX"]][order(ecoreg)],data.spain[["CY"]][order(ecoreg)],pty=".",cex=.2, col = rep(mycols,as.vector(table(ecoreg)))); 
legend("topleft", col = mycols, legend = levels(data.spain$eco_code), pch = rep(19,length(levels(ecoreg))),cex=2)
points(data.spain[["CX"]][ecoreg == 9],data.spain[["CY"]][ecoreg == 9],pty=".",cex=.5, col = "black"); ## Highlight the "rare" ecoregions
points(data.spain[["CX"]][ecoreg == 1],data.spain[["CY"]][ecoreg == 1],pty=".",cex=.5, col = "black"); ## Highlight the "rare" ecoregions
## PA1219 looks to be similar to PA1209; merge them together
data.spain$eco_codemerged <- combine_factor(data.spain$eco_code, c(1:8,6,9))
data.spain <- data.spain[-which(data.spain$eco_codemerged == ""),]

######################
## PERCENT DEAD
###################
## variable percent dead/cannot do with since dead variable is missing
###compute numer of dead per plot to remove plot with disturbance
perc.dead <- tapply(data.spain[["dead"]],INDEX=data.spain[["plot"]],FUN=function.perc.dead)
table(data.spain$dead)
## VARIABLE TO SELECT PLOT WITH NOT BIG DISTURBANCE KEEP OFTHER VARIABLES IF AVAILABLE (disturbance record)
data.spain <- merge(data.spain,data.frame(plot=as.numeric(names(perc.dead)),perc.dead=perc.dead),sort=FALSE, by = "plot")

###########################################################
### PLOT SELECTION FOR THE ANALYSIS
###################
## Remove data with mortality == 1 or 2
table(data.spain$Mortality_Cut)
data.spain <- subset(data.spain,subset= (data.spain[["Mortality_Cut"]] == 0 |  data.spain[["Mortality_Cut"]] == ""))

colnames(data.spain)[colnames(data.spain) %in% c("mat","pp","PET")] <- c("MAT","PP","PET")
colnames(data.spain)[names(data.spain) =="eco_codemerged"] <- c("ecocode")
vec.abio.var.names <-  c("MAT","PP","PET")
vec.basic.var <-  c("tree.id","sp","sp.name","plot","ecocode","D","G","dead","year","htot","Lon","Lat","perc.dead")
data.tree <- subset(data.spain,select=c(vec.basic.var,vec.abio.var.names))

##############################################
## COMPUTE MATRIX OF COMPETITION INDEX WITH SUM OF BA PER SPECIES IN EACH PLOT in m^2/ha without the target species
###########################
data.BA.SP <- BA.SP.FUN(id.tree=as.vector(data.spain[["tree.id"]]), diam=as.vector(data.spain[["D"]]),
	sp=as.vector(data.spain[["sp"]]), id.plot=as.vector(data.spain[["plot"]]),
	weights=as.vector(1/(pi*(data.spain[["R1"]])^2)), weight.full.plot=1/(pi*(25)^2))

## change NA and <0 data for 0
data.BA.SP[which(is.na(data.BA.SP),arr.ind=TRUE)] <- 0
data.BA.SP[,-1][which(data.BA.SP[,-1]<0,arr.ind=TRUE)] <- 0

### CHECK IF sp and sp name for column are the same
if(sum(!(names(data.BA.SP)[-1] %in% unique(data.spain[["sp"]]))) >0) stop("competition index sp name not the same as in data.tree")

#### compute BA tot for all competitors
BATOT.COMPET <- apply(data.BA.SP[,-1],MARGIN=1,FUN=sum,na.rm=TRUE)
data.BA.SP$BATOT.COMPET <- BATOT.COMPET
### create data frame
names(data.BA.SP) <- c("tree.id",names(data.BA.SP)[-1])
data.BA.sp <- merge(data.frame(tree.id=dataIFN.spain[["tree.id"]],ecocode=dataIFN.spain[["ecocode"]]),data.BA.SP,by="tree.id",sort=FALSE)
## test
if( sum(! data.BA.sp[["tree.id"]] == data.tree[["tree.id"]]) >0) stop("competition index not in the same order than data.tree")

## save everything as a list
list.spain <- list(data.tree=data.tree,data.BA.SP=data.BA.sp,data.traits=data.traits)
save(list.spain,file="./data/process/list.spain.Rdata")